计算机应用研究2016，Vol.33Issue(12)：3817-3821,5.DOI:10.3969/j.issn.1001-3695.2016.12.066
基于独立成分分析与核典型相关分析的WLA N室内定位方法
WLAN indoor positioning algorithms based on independent component analysis and kernel canonical correlation analysis
摘要
Abstract
The time-varying character of received signal strength (RSS)in WLAN positioning environment drastically reduces the relevance between RSS signal and position information which result in the low positioning accuracy.Based on this situa-tion,this paper used independent component analysis (ICA)algorithm to reduce the dimensions of RSS signal and their corre-lation,and extracted the independent components;then applied kernel canonical correlation analysis (KCCA)to extract the most correlated canonical features between the independent components of RSS signal and position information;finally em-ployed the traditional positioning algorithms such as weighted K nearest neighbors (WKNN),support vector machine (SVM) to localization.The experimental results show that with the deployment of ICA and KCCA to extract correlated canonical fea-tures,traditional WKNN and SVM positioning algorithms achieve better localization accuracy.关键词
无线局域网/室内定位/接收信号强度/独立成分分析/核典型相关分析Key words
WLAN/indoor positioning/received signal strength/independent component analysis/kernel canonical correla-tion analysis分类
信息技术与安全科学引用本文复制引用
张勇,史雅楠,黄杰,李飞腾..基于独立成分分析与核典型相关分析的WLA N室内定位方法[J].计算机应用研究,2016,33(12):3817-3821,5.基金项目
国家科技支撑计划资助项目 （）
